high-powertheme

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as 'training data', in order to make predictions or decisions without being explicitly programmed to perform the task.^[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.^[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.

• 1Overview
• 2History and relationships to other fields
• 4Approaches
  • 4.1Types of learning algorithms
  • 4.2Processes and techniques
  • 4.3Models
• 6Limitations
• 9Software

Overview[edit]

The name machine learning was coined in 1959 by Arthur Samuel.^[5]Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'^[6] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper 'Computing Machinery and Intelligence', in which the question 'Can machines think?' is replaced with the question 'Can machines do what we (as thinking entities) can do?'.^[7] In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.

Machine learning tasks[edit]

Machine learning tasks are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object (the input), and each image would have a label (the output) designating whether it contained the object. In special cases, the input may be only partially available, or restricted to special feedback.^{[clarification needed]}Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn't have labels.

Classification algorithms and regression algorithms are types of supervised learning. Classification algorithms are used when the outputs are restricted to a limited set of values. For a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. For an algorithm that identifies spam emails, the output would be the prediction of either 'spam' or 'not spam', represented by the Boolean values true and false. Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.

In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of 'features', or inputs, in a set of data.

Active learning algorithms access the desired outputs (training labels) for a limited set of inputs based on a budget, and optimize the choice of inputs for which it will acquire training labels. When used interactively, these can be presented to a human user for labeling. Reinforcement learning algorithms are given feedback in the form of positive or negative reinforcement in a dynamic environment, and are used in autonomous vehicles or in learning to play a game against a human opponent.^[2]:3 Other specialized algorithms in machine learning include topic modeling, where the computer program is given a set of natural language documents and finds other documents that cover similar topics. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Meta learning algorithms learn their own inductive bias based on previous experience. In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies, and imitation.^{[clarification needed]}

History and relationships to other fields[edit]

Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term 'Machine Learning' in 1959 while at IBM^[8]. As a scientific endeavour, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed 'neural networks'; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.^[9]Probabilistic reasoning was also employed, especially in automated medical diagnosis.^[10]:488

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.^[10]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.^[11] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.^{[10]:708–710; 755} Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as 'connectionism', by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.^[10]:25

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.^[11] It also benefited from the increasing availability of digitized information, and the ability to distribute it via the Internet.

Relation to data mining[edit]

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as 'unsupervised learning' or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Relation to optimization[edit]

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.^[12]

Relation to statistics[edit]

Machine learning and statistics are closely related fields. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.^[13] He also suggested the term data science as a placeholder to call the overall field.^[13]

Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model,^[14] wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.^[15]

Theory[edit]

A core objective of a learner is to generalize from its experience.^[2][16] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has underfit the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.^[17]

In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

Approaches[edit]

Types of learning algorithms[edit]

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised and semi-supervised learning[edit]

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.^[18] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and a desired output, also known as a supervisory signal. In the case of semi-supervised learning algorithms, some of the training examples are missing the desired output. In the mathematical model, each training example is represented by an array or vector, and the training data by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.^[19] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.^[6]

Supervised learning algorithms include classification and regression.^[20] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

Unsupervised learning[edit]

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics,^[21] though unsupervised learning encompasses other domains involving summarizing and explaining data features.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.

Reinforcement learning[edit]

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms.^[22][23] In machine learning, the environment is typically represented as a Markov Decision Process (MDP). Many reinforcement learning algorithms use dynamic programming techniques.^[22][23][24] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible.^[22][23] Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

Processes and techniques[edit]

Various processes, techniques and methods can be applied to one or more types of machine learning algorithms to enhance their performance.

Feature learning[edit]

Several learning algorithms aim at discovering better representations of the inputs provided during training.^[25] Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown and 'abnormal' and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model.

Decision trees[edit]

Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.

Association rules[edit]

Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of 'interestingness'.^[38]

Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.^[39] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.^[40] For example, the rule ${\displaystyle \{\mathrm{o}\mathrm{n}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s},\mathrm{p}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{e}\mathrm{s}\}\Rightarrow \{\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{r}\}}$ found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.

Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.^[41]

Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.^[42][43][44] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.^[45] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.

Models[edit]

Artificial neural networks[edit]

Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems 'learn' to perform tasks by considering examples, generally without being programmed with any task-specific rules.

An ANN is a model based on a collection of connected units or nodes called 'artificial neurons', which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a 'signal', from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called 'edges'. Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.^[46]

Support vector machines[edit]

Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.^[47] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

Bayesian networks[edit]

A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

Genetic algorithms[edit]

A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.^[48][49] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.^[50]

Applications[edit]

There are many applications for machine learning, including:

• Credit-card fraud detection
• DNA sequence classification
• Financial market analysis
• Internet fraud detection

In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.^[51] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly.^[52] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.^[53] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.^[54] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings, and that it may have revealed previously unrecognized influences between artists.^[55]In 2019 Springer Nature published the first research book created using machine learning.^[56]

Limitations[edit]

Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.^[57][58][59] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.^[60]

In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.^[61] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment.^[62][63]

Bias[edit]

Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.^[64] Language models learned from data have been shown to contain human-like biases.^[65][66] Machine learning systems used for criminal risk assessment have been found to be biased against black people.^[67][68] In 2015, Google photos would often tag black people as gorillas,^[69] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorilla from the training data, and thus was not able to recognize real gorillas at all.^[70] Similar issues with recognizing non-white people have been found in many other systems.^[71] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.^[72] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.^[73] Concern for reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that 'There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”^[74]

Model assessments[edit]

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.^[75]

In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the False Positive Rate (FPR) as well as the False Negative Rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The Total Operating Characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used Receiver Operating Characteristic (ROC) and ROC's associated Area Under the Curve (AUC).^[76]

Ethics[edit]

Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.^[77] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.^[78][79] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

Because language contains biases, machines trained on language corpora will necessarily also learn bias.^[80]

Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest, but as income generating machines. This is especially true in the United States where there is a perpetual ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes in. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed.^[81]

Software[edit]

Software suites containing a variety of machine learning algorithms include the following:

Free and open-source software[edit]

• ROOT (TMVA with ROOT)
• Torch / PyTorch
• Weka / MOA

Proprietary software with free and open-source editions[edit]

Proprietary software[edit]

• Angoss KnowledgeSTUDIO
• STATISTICA Data Miner

Journals[edit]

Conferences[edit]

See also[edit]

References[edit]

1. ^The definition 'without being explicitly programmed' is often attributed to Arthur Samuel, who coined the term 'machine learning' in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer 'Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?' in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.
2. ^ ^abcdBishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer, ISBN978-0-387-31073-2
3. ^Machine learning and pattern recognition 'can be viewed as two facets of the same field.'^[2]:vii
4. ^Friedman, Jerome H. (1998). 'Data Mining and Statistics: What's the connection?'. Computing Science and Statistics. 29 (1): 3–9.
5. ^Samuel, Arthur (1959). 'Some Studies in Machine Learning Using the Game of Checkers'. IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX10.1.1.368.2254. doi:10.1147/rd.33.0210.
6. ^ ^abMitchell, T. (1997). Machine Learning. McGraw Hill. p. 2. ISBN978-0-07-042807-2.
7. ^Harnad, Stevan (2008), 'The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence', in Epstein, Robert; Peters, Grace (eds.), The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer, Kluwer
8. ^R. Kohavi and F. Provost, 'Glossary of terms,' Machine Learning, vol. 30, no. 2–3, pp. 271–274, 1998.
9. ^Sarle, Warren (1994). 'Neural Networks and statistical models'. CiteSeerX10.1.1.27.699.
10. ^ ^abcdRussell, Stuart; Norvig, Peter (2003) [1995]. Artificial Intelligence: A Modern Approach (2nd ed.). Prentice Hall. ISBN978-0137903955.
11. ^ ^abLangley, Pat (2011). 'The changing science of machine learning'. Machine Learning. 82 (3): 275–279. doi:10.1007/s10994-011-5242-y.
12. ^Le Roux, Nicolas; Bengio, Yoshua; Fitzgibbon, Andrew (2012). 'Improving First and Second-Order Methods by Modeling Uncertainty'. In Sra, Suvrit; Nowozin, Sebastian; Wright, Stephen J. (eds.). Optimization for Machine Learning. MIT Press. p. 404.
13. ^ ^abMichael I. Jordan (2014-09-10). 'statistics and machine learning'. reddit. Retrieved 2014-10-01.
14. ^Cornell University Library. 'Breiman: Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)'. Retrieved 8 August 2015.
15. ^Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. p. vii.
16. ^Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. USA, Massachusetts: MIT Press. ISBN9780262018258.
17. ^Alpaydin, Ethem (2010). Introduction to Machine Learning. London: The MIT Press. ISBN978-0-262-01243-0. Retrieved 4 February 2017.
18. ^Russell, Stuart J.; Norvig, Peter (2010). Artificial Intelligence: A Modern Approach (Third ed.). Prentice Hall. ISBN9780136042594.
19. ^Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. The MIT Press. ISBN9780262018258.
20. ^Alpaydin, Ethem (2010). Introduction to Machine Learning. MIT Press. p. 9. ISBN978-0-262-01243-0.
21. ^Jordan, Michael I.; Bishop, Christopher M. (2004). 'Neural Networks'. In Allen B. Tucker (ed.). Computer Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton, Florida: Chapman & Hall/CRC Press LLC. ISBN978-1-58488-360-9.
22. ^ ^abcDimitri P. Bertsekas. 'Dynamic Programming and Optimal Control: Approximate Dynamic Programming, Vol.II', Athena Scientific, 2012,[1]
23. ^ ^abcDimitri P. Bertsekas and John N. Tsitsiklis. 'Neuro-Dynamic Programming', Athena Scientific, 1996,[2]
24. ^van Otterlo, M.; Wiering, M. (2012). Reinforcement learning and markov decision processes. Reinforcement Learning. Adaptation, Learning, and Optimization. 12. pp. 3–42. doi:10.1007/978-3-642-27645-3_1. ISBN978-3-642-27644-6.
25. ^Y. Bengio; A. Courville; P. Vincent (2013). 'Representation Learning: A Review and New Perspectives'. IEEE Trans. PAMI, Special Issue Learning Deep Architectures. 35 (8): 1798–1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50. PMID23787338.
26. ^Nathan Srebro; Jason D. M. Rennie; Tommi S. Jaakkola (2004). Maximum-Margin Matrix Factorization. NIPS.
27. ^Coates, Adam; Lee, Honglak; Ng, Andrew Y. (2011). An analysis of single-layer networks in unsupervised feature learning(PDF). Int'l Conf. on AI and Statistics (AISTATS).
28. ^Csurka, Gabriella; Dance, Christopher C.; Fan, Lixin; Willamowski, Jutta; Bray, Cédric (2004). Visual categorization with bags of keypoints(PDF). ECCV Workshop on Statistical Learning in Computer Vision.
29. ^Daniel Jurafsky; James H. Martin (2009). Speech and Language Processing. Pearson Education International. pp. 145–146.
30. ^Lu, Haiping; Plataniotis, K.N.; Venetsanopoulos, A.N. (2011). 'A Survey of Multilinear Subspace Learning for Tensor Data'(PDF). Pattern Recognition. 44 (7): 1540–1551. doi:10.1016/j.patcog.2011.01.004.
31. ^Yoshua Bengio (2009). Learning Deep Architectures for AI. Now Publishers Inc. pp. 1–3. ISBN978-1-60198-294-0.
32. ^Tillmann, A. M. (2015). 'On the Computational Intractability of Exact and Approximate Dictionary Learning'. IEEE Signal Processing Letters. 22 (1): 45–49. arXiv:1405.6664. Bibcode:2015ISPL...22...45T. doi:10.1109/LSP.2014.2345761.
33. ^Aharon, M, M Elad, and A Bruckstein. 2006. 'K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation.' Signal Processing, IEEE Transactions on 54 (11): 4311–4322
34. ^Zimek, Arthur; Schubert, Erich (2017), 'Outlier Detection', Encyclopedia of Database Systems, Springer New York, pp. 1–5, doi:10.1007/978-1-4899-7993-3_80719-1, ISBN9781489979933
35. ^Hodge, V. J.; Austin, J. (2004). 'A Survey of Outlier Detection Methodologies'(PDF). Artificial Intelligence Review. 22 (2): 85–126. CiteSeerX10.1.1.318.4023. doi:10.1007/s10462-004-4304-y.
36. ^Dokas, Paul; Ertoz, Levent; Kumar, Vipin; Lazarevic, Aleksandar; Srivastava, Jaideep; Tan, Pang-Ning (2002). 'Data mining for network intrusion detection'(PDF). Proceedings NSF Workshop on Next Generation Data Mining.
37. ^Chandola, V.; Banerjee, A.; Kumar, V. (2009). 'Anomaly detection: A survey'. ACM Computing Surveys. 41 (3): 1–58. doi:10.1145/1541880.1541882.
38. ^Piatetsky-Shapiro, Gregory (1991), Discovery, analysis, and presentation of strong rules, in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, MA.
39. ^Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Bacardit, Jaume (2011-09-01). 'Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets'. The Plant Cell. 23 (9): 3101–3116. doi:10.1105/tpc.111.088153. ISSN1532-298X. PMC3203449. PMID21896882.
40. ^Agrawal, R.; Imieliński, T.; Swami, A. (1993). 'Mining association rules between sets of items in large databases'. Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93. p. 207. CiteSeerX10.1.1.40.6984. doi:10.1145/170035.170072. ISBN978-0897915922.
41. ^Urbanowicz, Ryan J.; Moore, Jason H. (2009-09-22). 'Learning Classifier Systems: A Complete Introduction, Review, and Roadmap'. Journal of Artificial Evolution and Applications. 2009: 1–25. doi:10.1155/2009/736398. ISSN1687-6229.
42. ^Plotkin G.D. Automatic Methods of Inductive Inference, PhD thesis, University of Edinburgh, 1970.
43. ^Shapiro, Ehud Y. Inductive inference of theories from facts, Research Report 192, Yale University, Department of Computer Science, 1981. Reprinted in J.-L. Lassez, G. Plotkin (Eds.), Computational Logic, The MIT Press, Cambridge, MA, 1991, pp. 199–254.
44. ^Shapiro, Ehud Y. (1983). Algorithmic program debugging. Cambridge, Mass: MIT Press. ISBN0-262-19218-7
45. ^Shapiro, Ehud Y. 'The model inference system.' Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2. Morgan Kaufmann Publishers Inc., 1981.
46. ^Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng. 'Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations' Proceedings of the 26th Annual International Conference on Machine Learning, 2009.
47. ^Cortes, Corinna; Vapnik, Vladimir N. (1995). 'Support-vector networks'. Machine Learning. 20 (3): 273–297. doi:10.1007/BF00994018.
48. ^Goldberg, David E.; Holland, John H. (1988). 'Genetic algorithms and machine learning'. Machine Learning. 3 (2): 95–99. doi:10.1007/bf00113892.
49. ^Michie, D.; Spiegelhalter, D. J.; Taylor, C. C. (1994). 'Machine Learning, Neural and Statistical Classification'. Ellis Horwood Series in Artificial Intelligence. Bibcode:1994mlns.book.....M.
50. ^Zhang, Jun; Zhan, Zhi-hui; Lin, Ying; Chen, Ni; Gong, Yue-jiao; Zhong, Jing-hui; Chung, Henry S.H.; Li, Yun; Shi, Yu-hui (2011). 'Evolutionary Computation Meets Machine Learning: A Survey'(PDF). Computational Intelligence Magazine. 6 (4): 68–75. doi:10.1109/mci.2011.942584.
51. ^'BelKor Home Page' research.att.com
52. ^'The Netflix Tech Blog: Netflix Recommendations: Beyond the 5 stars (Part 1)'. 2012-04-06. Retrieved 8 August 2015.
53. ^Scott Patterson (13 July 2010). 'Letting the Machines Decide'. The Wall Street Journal. Retrieved 24 June 2018.
54. ^Vinod Khosla (January 10, 2012). 'Do We Need Doctors or Algorithms?'. Tech Crunch.
55. ^When A Machine Learning Algorithm Studied Fine Art Paintings, It Saw Things Art Historians Had Never Noticed, The Physics at ArXiv blog
56. ^Vincent, James (2019-04-10). 'The first AI-generated textbook shows what robot writers are actually good at'. The Verge. Retrieved 2019-05-05.
57. ^'Why Machine Learning Models Often Fail to Learn: QuickTake Q&A'. Bloomberg.com. 2016-11-10. Retrieved 2017-04-10.
58. ^'The First Wave of Corporate AI Is Doomed to Fail'. Harvard Business Review. 2017-04-18. Retrieved 2018-08-20.
59. ^'Why the A.I. euphoria is doomed to fail'. VentureBeat. 2016-09-18. Retrieved 2018-08-20.
60. ^'9 Reasons why your machine learning project will fail'. www.kdnuggets.com. Retrieved 2018-08-20.
61. ^'Why Uber's self-driving car killed a pedestrian'. The Economist. Retrieved 2018-08-20.
62. ^'IBM's Watson recommended 'unsafe and incorrect' cancer treatments - STAT'. STAT. 2018-07-25. Retrieved 2018-08-21.
63. ^Hernandez, Daniela; Greenwald, Ted (2018-08-11). 'IBM Has a Watson Dilemma'. Wall Street Journal. ISSN0099-9660. Retrieved 2018-08-21.
64. ^Garcia, Megan (2016). 'Racist in the Machine'. World Policy Journal. 33 (4): 111–117. doi:10.1215/07402775-3813015. ISSN0740-2775.
65. ^Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind (2017-04-14). 'Semantics derived automatically from language corpora contain human-like biases'. Science. 356 (6334): 183–186. arXiv:1608.07187. Bibcode:2017Sci...356..183C. doi:10.1126/science.aal4230. ISSN0036-8075. PMID28408601.
66. ^Wang, Xinan; Dasgupta, Sanjoy (2016), Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I. (eds.), 'An algorithm for L1 nearest neighbor search via monotonic embedding'(PDF), Advances in Neural Information Processing Systems 29, Curran Associates, Inc., pp. 983–991, retrieved 2018-08-20
67. ^'Machine Bias'. ProPublica. Julia Angwin, Jeff Larson, Lauren Kirchner, Surya Mattu. 2016-05-23. Retrieved 2018-08-20.CS1 maint: others (link)
68. ^'Opinion When an Algorithm Helps Send You to Prison'. New York Times. Retrieved 2018-08-20.
69. ^'Google apologises for racist blunder'. BBC News. 2015-07-01. Retrieved 2018-08-20.
70. ^'Google 'fixed' its racist algorithm by removing gorillas from its image-labeling tech'. The Verge. Retrieved 2018-08-20.
71. ^'Opinion Artificial Intelligence's White Guy Problem'. New York Times. Retrieved 2018-08-20.
72. ^Metz, Rachel. 'Why Microsoft's teen chatbot, Tay, said lots of awful things online'. MIT Technology Review. Retrieved 2018-08-20.
73. ^Simonite, Tom. 'Microsoft says its racist chatbot illustrates how AI isn't adaptable enough to help most businesses'. MIT Technology Review. Retrieved 2018-08-20.
74. ^Hempel, Jessi (2018-11-13). 'Fei-Fei Li's Quest to Make Machines Better for Humanity'. Wired. ISSN1059-1028. Retrieved 2019-02-17.
75. ^Kohavi, Ron (1995). 'A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection'(PDF). International Joint Conference on Artificial Intelligence.
76. ^Pontius, Robert Gilmore; Si, Kangping (2014). 'The total operating characteristic to measure diagnostic ability for multiple thresholds'. International Journal of Geographical Information Science. 28 (3): 570–583. doi:10.1080/13658816.2013.862623.
77. ^Bostrom, Nick (2011). 'The Ethics of Artificial Intelligence'(PDF). Retrieved 11 April 2016.
78. ^Edionwe, Tolulope. 'The fight against racist algorithms'. The Outline. Retrieved 17 November 2017.
79. ^Jeffries, Adrianne. 'Machine learning is racist because the internet is racist'. The Outline. Retrieved 17 November 2017.
80. ^Narayanan, Arvind (August 24, 2016). 'Language necessarily contains human biases, and so will machines trained on language corpora'. Freedom to Tinker.
81. ^Char, D. S.; Shah, N. H.; Magnus, D. (2018). 'Implementing Machine Learning in Health Care—Addressing Ethical Challenges'. New England Journal of Medicine. 378 (11): 981–983. doi:10.1056/nejmp1714229. PMC5962261. PMID29539284.

Further reading[edit]

• Nils J. Nilsson, Introduction to Machine Learning.
• Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning, Springer. ISBN0-387-95284-5.
• Pedro Domingos (September 2015), The Master Algorithm, Basic Books, ISBN978-0-465-06570-7
• Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN978-0-12-374856-0.
• Ethem Alpaydin (2004). Introduction to Machine Learning, MIT Press, ISBN978-0-262-01243-0.
• David J. C. MacKay. Information Theory, Inference, and Learning Algorithms Cambridge: Cambridge University Press, 2003. ISBN0-521-64298-1
• Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN0-471-05669-3.
• Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN0-19-853864-2.
• Stuart Russell & Peter Norvig, (2009). Artificial Intelligence – A Modern Approach. Pearson, ISBN9789332543515.
• Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
• Ray Solomonoff, An Inductive Inference Machine A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
• Artificial Intelligence: A Modern Approach (3rd Edition)

External links[edit]

• mloss is an academic database of open-source machine learning software.
• Machine Learning Crash Course by Google. This is a free course on machine learning through the use of TensorFlow.

Experiential learning is the process of learning through experience, and is more specifically defined as 'learning through reflection on doing'.^[1]Hands-on learning is a form of experiential learning but does not necessarily involve students reflecting on their product.^[2][3][4] Experiential learning is distinct from rote or didactic learning, in which the learner plays a comparatively passive role.^[5] It is related to, but not synonymous with, other forms of active learning such as action learning, adventure learning, free-choice learning, cooperative learning, service-learning, and situated learning.^[6]

Experiential learning is often used synonymously with the term 'experiential education', but while experiential education is a broader philosophy of education, experiential learning considers the individual learning process.^[7] As such, compared to experiential education, experiential learning is concerned with more concrete issues related to the learner and the learning context.

The general concept of learning through experience is ancient. Around 350 BCE, Aristotle wrote in the Nicomachean Ethics 'for the things we have to learn before we can do them, we learn by doing them'.^[8] But as an articulated educational approach, experiential learning is of much more recent vintage. Beginning in the 1970s, David A. Kolb helped to develop the modern theory of experiential learning, drawing heavily on the work of John Dewey, Kurt Lewin, and Jean Piaget.^[9]

Experiential learning has significant teaching advantages. Peter Senge, author of The Fifth Discipline (1990), states that teaching is of utmost importance to motivate people. Learning only has good effects when learners have the desire to absorb the knowledge. Therefore, experiential learning requires the showing of directions for learners.^[10]

Experiential learning entails a hands-on approach to learning that moves away from just the teacher at the front of the room emparting and transferring their knowledge to students. It makes learning an experience that moves beyond the classroom and strives to bring a more involved way of learning.

• 1Kolb's experiential learning model
• 8See also

Kolb's experiential learning model[edit]

Experiential learning focuses on the learning process for the individual. One example of experiential learning is going to the zoo and learning through observation and interaction with the zoo environment, as opposed to reading about animals from a book. Thus, one makes discoveries and experiments with knowledge firsthand, instead of hearing or reading about others' experiences. Likewise, in business school, internship, and job-shadowing, opportunities in a student’s field of interest can provide valuable experiential learning which contributes significantly to the student’s overall understanding of the real-world environment.^[11]

A third example of experiential learning involves learning how to ride a bike,^[12] a process which can illustrate the four-step experiential learning model (ELM) as set forth by Kolb^[13] and outlined in Figure 1 below. Following this example, in the 'concrete experience' stage, the learner physically experiences the bike in the 'here-and-now'.^[14] This experience forms 'the basis for observation and reflection' and the learner has the opportunity to consider what is working or failing (reflective observation), and to think about ways to improve on the next attempt made at riding (abstract conceptualization). Every new attempt to ride is informed by a cyclical pattern of previous experience, thought and reflection (active experimentation).^[14]

Figure 1 – David Kolb's Experiential Learning Model (ELM)^[15]

Elements[edit]

Experiential learning can exist without a teacher and relates solely to the meaning-making process of the individual's direct experience. However, though the gaining of knowledge is an inherent process that occurs naturally, a genuine learning experience requires certain elements.^[6] According to Kolb, knowledge is continuously gained through both personal and environmental experiences.^[16] Kolb states that in order to gain genuine knowledge from an experience, the learner must have four abilities:

• The learner must be willing to be actively involved in the experience;
• The learner must be able to reflect on the experience;
• The learner must possess and use analytical skills to conceptualize the experience; and
• The learner must possess decision making and problem solving skills in order to use the new ideas gained from the experience.

Implementation[edit]

Experiential learning requires self-initiative, an 'intention to learn' and an 'active phase of learning'.^[17] Kolb's cycle of experiential learning can be used as a framework for considering the different stages involved.^[18] Jennifer A. Moon has elaborated on this cycle to argue that experiential learning is most effective when it involves: 1) a 'reflective learning phase' 2) a phase of learning resulting from the actions inherent to experiential learning, and 3) 'a further phase of learning from feedback'.^[17] This process of learning can result in 'changes in judgment, feeling or skills' for the individual^[19] and can provide direction for the 'making of judgments as a guide to choice and action'.^[20]

Most educators understand the important role experience plays in the learning process. The role of emotion and feelings in learning from experience has been recognised as an important part of experiential learning.^[17] While those factors may improve the likelihood of experiential learning occurring, it can occur without them. Rather, what is vital in experiential learning is that the individual is encouraged to directly involve themselves in the experience, and then to reflect on their experiences using analytic skills, in order that they gain a better understanding of the new knowledge and retain the information for a longer time.

Reflection is a crucial part of the experiential learning process, and like experiential learning itself, it can be facilitated or independent. Dewey wrote that 'successive portions of reflective thought grow out of one another and support one another', creating a scaffold for further learning, and allowing for further experiences and reflection.^[21] This reinforces the fact that experiential learning and reflective learning are iterative processes, and the learning builds and develops with further reflection and experience. Facilitation of experiential learning and reflection is challenging, but 'a skilled facilitator, asking the right questions and guiding reflective conversation before, during, and after an experience, can help open a gateway to powerful new thinking and learning'.^[22] Jacobson and Ruddy, building on Kolb's four-stage Experiential Learning Model^[14] and Pfeiffer and Jones's five stage Experiential Learning Cycle,^[23] took these theoretical frameworks and created a simple, practical questioning model for facilitators to use in promoting critical reflection in experiential learning. Their '5 Questions' model is as follows:^[22]

• Did you notice?
• Why did that happen?
• Does that happen in life?
• Why does that happen?
• How can you use that?

These questions are posed by the facilitator after an experience, and gradually lead the group towards a critical reflection on their experience, and an understanding of how they can apply the learning to their own life.^[22] Although the questions are simple, they allow a relatively inexperienced facilitator to apply the theories of Kolb, Pfeiffer, and Jones, and deepen the learning of the group.

While it is the learner's experience that is most important to the learning process, it is also important not to forget the wealth of experience a good facilitator also brings to the situation. However, while a facilitator, or 'teacher', may improve the likelihood of experiential learning occurring, a facilitator is not essential to experiential learning. Rather, the mechanism of experiential learning is the learner's reflection on experiences using analytic skills. This can occur without the presence of a facilitator, meaning that experiential learning is not defined by the presence of a facilitator. Yet, by considering experiential learning in developing course or program content, it provides an opportunity to develop a framework for adapting varying teaching/learning techniques into the classroom.^[24]

In schools[edit]

Experiential learning is supported in different school organizational models and learning environments.

• Think Global School is a four-year traveling high school that holds classes in a new country each term. Students engage in experiential learning through activities such as workshops, cultural exchanges, museum tours, and nature expeditions.
• The Dawson School in Boulder, Colorado, devotes two weeks of each school year to experiential learning, with students visiting surrounding states to engage in community service, visit museums and scientific institutions, and engage in activities such as mountain biking, backpacking, and canoeing.
• In the ELENA-Project, the follow-up project of 'animals live', experiential learning with living animals will be developed. Together with project partners from Romania, Hungary and Georgia, the Bavarian Academy of Nature Conservation and Landscape Management in Germany brings living animals in the lessons of European schools. The aim is to brief children for the context of the biological diversity and to support them to develop ecologically oriented values.^[25]
• Loving High School in Loving, New Mexico, publishes career and technical education opportunities for students. These include internship for students who are interested in science, STEM majors, or architecture. The school is making good connections with local businesses, which helps students get used to working in such environments.
• Lake View High School in Chicago, Illinois is the institution which offers early college credits for students. It trains students with majors such as STEM, humanities, music/ art, and languages.^[26]
• Robert H. Smith School of Business offers select undergraduate students a year-round advanced course whereby students conduct financial analyses and security trades on Bloomberg Terminals to manage real investment dollars in the Lemma Senbet Fund.

In business education[edit]

As higher education continues to adapt to new expectations from students, experiential learning in business and accounting programs has become more important. For example, Clark & White (2010) point out that 'a quality university business education program must include an experiential learning component'.^[27] With reference to this study, employers note that graduating students need to build skills in 'professionalism' – which can be taught via experiential learning. Students also value this learning as much as industry.

Learning styles also impact business education in the classroom. Kolb transposes four learning styles, Diverger, Assimilator, Accommodator and Converger, atop the Experiential Learning Model, using the four experiential learning stages to carve out 'four quadrants', one for each learning style. An individual’s dominant learning style can be identified by taking Kolb’s Learning Style Inventory (LSI). Robert Loo (2002) undertook a meta-analysis of 8 studies which revealed that Kolb’s learning styles were not equally distributed among business majors in the sample.^[28] More specifically, results indicated that there appears to be a high proportion of assimilators and a lower proportion of accommodators than expected for business majors. Not surprisingly, within the accounting sub-sample there was a higher proportion of convergers and a lower proportion of accommodates. Similarly, in the finance sub-sample, a higher proportion of assimilators and lower proportion of divergers was apparent. Within the marketing sub-sample there was an equal distribution of styles. This would provide some evidence to suggest that while it is useful for educators to be aware of common learning styles within business and accounting programs, they should be encouraging students to use all four learning styles appropriately and students should use a wide range of learning methods.^[28]

Professional education applications, also known as management training or organizational development, apply experiential learning techniques in training employees at all levels within the business and professional environment. Interactive, role-play based customer service training is often used in large retail chains.^[29] Training board games simulating business and professional situations such as the Beer Distribution Game used to teach supply chain management, and the Friday Night at the ER game used to teach systems thinking, are used in business training efforts.^[30]

In business[edit]

Experiential business learning is the process of learning and developing business skills through the medium of shared experience. The main point of difference between this and academic learning is more “real-life” experience for the recipient.^[31][32][33]

This may include for example, learning gained from a network of business leaders sharing best practice, or individuals being mentored or coached by a person who has faced similar challenges and issues, or simply listening to an expert or thought leader in current business thinking.

Providers of this type of experiential business learning often include membership organisations who offer product offerings such as peer group learning, professional business networking, expert/speaker sessions, mentoring and/or coaching.

Comparisons[edit]

Experiential learning is most easily compared with academic learning, the process of acquiring information through the study of a subject without the necessity for direct experience. While the dimensions of experiential learning are analysis, initiative, and immersion, the dimensions of academic learning are constructive learning and reproductive learning.^[35] Though both methods aim at instilling new knowledge in the learner, academic learning does so through more abstract, classroom-based techniques, whereas experiential learning actively involves the learner in a concrete experience.

Benefits[edit]

• Experience real world: For example, students who major in Chemistry may have chances to interact with the chemical environment. Learners who have a desire to become businesspeople will have the opportunity to experience the manager position
• Opportunities for creativity: There is always more than one solution for a problem in the real world. Students will have a better chance to learn that lesson when they get to interact with real life experiences^[36]

See also[edit]

People[edit]

Subjects[edit]

• Sudbury model of democratic education

References[edit]

1. ^Felicia, Patrick (2011). Handbook of Research on Improving Learning and Motivation. p. 1003. ISBN1609604962.
2. ^The Out of Eden Walk: An Experiential Learning Journey from the Virtual to the Real, Edutopia, January 3, 2014. Retrieved 2016-03-16
3. ^Action Learning - How does it work in practice? MIT Sloan Management. Retrieved 2016-03-16^{[dead link]}
4. ^The Power of Experiential Learning, 4-H Cooperative Curriculum System. Retrieved 2016-03-16^{[dead link]}
5. ^Beard, Colin (2010). The Experiential Learning Toolkit: Blending Practice with Concepts. p. 20. ISBN9780749459345.
6. ^ ^abItin, C. M. (1999). Reasserting the Philosophy of Experiential Education as a Vehicle for Change in the 21st Century. The Journal of Physical Education 22(2), p. 91-98.
7. ^Breunig, Mary C. (2009). 'Teaching Dewey's Experience and Education Experientially'. In Stremba, Bob; Bisson, Christian A. (eds.). Teaching Adventure Education Theory: Best Practices. p. 122. ISBN9780736071260.
8. ^Nicomachean Ethics, Book 2, Chase translation (1911).
9. ^Dixon, Nancy M.; Adams, Doris E.; Cullins, Richard (1997). 'Learning Style'. Assessment, Development, and Measurement. p. 41. ISBN9781562860493.
10. ^Hawtrey, Kim. 'Using Experiential Learning Techniques'.
11. ^McCarthy, P. R., & McCarthy, H. M. (2006). When Case Studies Are Not Enough: Integrating Experiential Learning Into Business Curricula. Journal of Education for Business, 81(4), pp. 201-204.
12. ^Kraft, R. G. (1994).Bike riding and the art of learning.In L. B. Barnes, C. Roland Christensen, & A. J. Hansen (Eds.), Teaching and the case method.Boston: Harvard Business School Press.
13. ^Loo, R. (2002). A Meta-Analytic Examination of Kolb's Learning Style Preferences Among Business Majors. Journal of Education for Business, 77:5, 252-256
14. ^ ^abcKolb, D. (1984). Experiential Learning: experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall. p. 21
15. ^http://www2.le.ac.uk/departments/gradschool/training/resources/teaching/theories/kolb Retrieved October 28, 2012.
16. ^Merriam, S. B., Caffarella, R. S., & Baumgartner, L. M. (2007). Learning in adulthood: a comprehensive guide. San Francisco: John Wiley & Sons, Inc.
17. ^ ^abcMoon, J. (2004). A Handbook of Reflective and Experiential Learning:Theory and Practice. London: Routledge Falmer. p. 126.
18. ^Kolb, D (1984). Experiential Learning as the Science of Learning and Development. Englewood Cliffs, NJ: Prentice Hall.
19. ^Chickering, A (1977). Experience and Learning. New York: Change Magazine Press. p. 63.
20. ^Hutton, M. (1980). Learning from action: a conceptual framework, in S. Warner Weil and M. McGill (eds) Making Sense of Experiential Learning. Milton Keynes: SRHE/Open University Press. pp. 50–9, p. 51.
21. ^Kompf, M., & Bond, R. (2001). Critical reflection in adult education. In T. Barer-Stein & M. Kompf (Eds.), The craft of teaching adults (p. 55). Toronto, ON: Irwin.
22. ^ ^abcJacobson, M. & Ruddy, M. (2004) Open to outcome (p. 2). Oklahoma City, OK: Wood 'N' Barnes.
23. ^Pfeiffer, W. & Jones, J. E. (1975). A Handbook of Structured Experiences for Human Relations Training. La Jolla, California: University Associates.
24. ^Rodrigues, C. A. (2004). The importance level of ten teaching/learning techniques as rated by university business students and instructors. Journal of Management Development, 23(2), 169-182.
25. ^ELENA project leader
26. ^Staff, Noodle. '41 Most Innovative K–12 Schools in America'. Retrieved 2015-10-19.
27. ^Clark, J., & White, G. (2010). 'Experiential Learning: A Definitive Edge In The Job Market'. American Journal of Business Education, 3(2), pp. 115-118.
28. ^ ^abLoo, R. (2002). 'A Meta-Analytic Examination of Kolb's Learning Style Preferences Among Business Majors'. Journal of Education for Business, 77:5, 252-256
29. ^https://multimediaplus.com/experiential-learning/
30. ^Faria, Anthony J. '4'(PDF). Business Simulation Games after Thirty Years: Current Usage Levels in the United States in Gentry (ed.) Guide to Business Gaming and Experiential Learning. The University of Michigan: Nichols/GP Pub., 1990. pp. 36–47. ISBN978-0893973698. Retrieved 12 March 2014.
31. ^'David A. Kolb on experiential learning'. infed.org. 2013-04-26. Retrieved 2018-10-15.
32. ^Greenaway, Tim Pickles and Roger. 'Experiential learning articles + critiques of David Kolb's theory'. www.reviewing.co.uk. Retrieved 2018-10-15.
33. ^Council, Young Entrepreneur. 'Seven Mentorship Methods And Opportunities Entrepreneurs Should Remember'. Forbes. Retrieved 2018-10-15.
34. ^Skill Pyramid
35. ^Stavenga de Jong, J. A., Wierstra, R. F. A. and Hermanussen, J. (2006) 'An exploration of the relationship between academic and experiential learning approaches in vocational education', British Journal of Educational Psychology. 76;1. pp. 155-169.
36. ^'The Benefits of Experiential Learning'.